Robotic Agents (CMPSC 311) Robot Navigation Janyl Jumadinova - PowerPoint PPT Presentation

Robotic Agents (CMPSC 311) Robot Navigation Janyl Jumadinova September 19, 2019 Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 1 / 31

Do we need to localize or not? To go from A to B , does the robot need to know where it is? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 2 / 31

Do we need to localize or not? To go from A to B , does the robot need to know where it is? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 2 / 31

Do we need to localize or not? How to navigate between A and B : navigation without hitting obstacles, detection of goal location. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 3 / 31

Do we need to localize or not? How to navigate between A and B : navigation without hitting obstacles, detection of goal location. Possible by always following the left wall Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 3 / 31

Do we need to localize or not? How to navigate between A and B : navigation without hitting obstacles, detection of goal location. Possible by always following the left wall - However, how to detect that the goal is reached? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 3 / 31

Do we need to localize or not? Following the left wall is an example of behavior-based navigation. It can work in some environments, but not in all. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 4 / 31

Do we need to localize or not? Following the left wall is an example of behavior-based navigation. It can work in some environments, but not in all. With which accuracy and reliability do we reach the goal? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 4 / 31

Do we need to localize or not? As opposed to behavior-based navigation is map-based navigation. Assuming that the map is known, at every time step the robot has to know where it is. How? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 5 / 31

Do we need to localize or not? As opposed to behavior-based navigation is map-based navigation. Assuming that the map is known, at every time step the robot has to know where it is. How? If we know the start position, we can use wheel odometry or previous position and estimated speed. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 5 / 31

Do we need to localize or not? As opposed to behavior-based navigation is map-based navigation. Assuming that the map is known, at every time step the robot has to know where it is. How? If we know the start position, we can use wheel odometry or previous position and estimated speed. Is this enough? What else can we use? Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 5 / 31

Do we need to localize or not? As opposed to behavior-based navigation is map-based navigation. Assuming that the map is known, at every time step the robot has to know where it is. How? If we know the start position, we can use wheel odometry or previous position and estimated speed. Is this enough? What else can we use? How do we represent the map for the robot? And how do we represent the position of the robot in the map? (return to this later) Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 5 / 31

How to localize? Localization based on external sensors, beacons or landmarks. Odometry. Map Based Localization (without external sensors or artificial landmarks; just using robot onboard sensors) - Example: Probabilistic Map Based Localization Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 6 / 31

Beacon Based Localization The beacons are fixed at appropriate locations in the environment. The precise locations of these beacons are known to the robot. As it moves, it uses some on-board device to measure its exact distance and direction from any one beacon. Hence the robot can calculate its own precise position in the environment. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 7 / 31

Beacon Based Localization The beacons are fixed at appropriate locations in the environment. The precise locations of these beacons are known to the robot. As it moves, it uses some on-board device to measure its exact distance and direction from any one beacon. Hence the robot can calculate its own precise position in the environment. Ex. 1: Poles with highly reflective surface and a laser for detecting them Ex. 2: Coloured beacons and an omnidirectional camera for detecting them (example: RoboCup or autonomous robots in tennis fields) Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 7 / 31

Odometry Dead reckoning (also deduced reckoning or odometry) is the process of calculating vehicle’s current position by using a previously determined position and estimated speeds over the elapsed time. Odometry : wheel sensors only Dead reckoning : also heading sensors Robot motion is recovered by integrating proprioceptive sensor velocities readings. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 8 / 31

Odometry Dead reckoning (also deduced reckoning or odometry) is the process of calculating vehicle’s current position by using a previously determined position and estimated speeds over the elapsed time. Odometry : wheel sensors only Dead reckoning : also heading sensors Robot motion is recovered by integrating proprioceptive sensor velocities readings. Pros : Straightforward Cons : Errors are integrated → unbound Heading sensors (e.g., gyroscope) help to reduce the accumulated errors but drift remains. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 8 / 31

The Problem Consider a mobile robot moving in a known environment. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 9 / 31

The Problem Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it might keep track of its location using odometry. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 9 / 31

The Problem Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it might keep track of its location using odometry. However, after a certain movement the robot will get very uncertain about its position → update using an observation of its environment. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 9 / 31

The Problem Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it might keep track of its location using odometry. However, after a certain movement the robot will get very uncertain about its position → update using an observation of its environment. Observation leads also to an estimate of the robot position which can then be fused with the odometric estimation to get the best possible update of the robots actual position. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 9 / 31

Map-based localization Consider a mobile robot moving in a known environment. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 10 / 31

Map-based localization Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it can keep track of its motion using odometry. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 11 / 31

Map-based localization Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it can keep track of its motion using odometry. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 12 / 31

Map-based localization Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it can keep track of its motion using odometry. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 13 / 31

Map-based localization Consider a mobile robot moving in a known environment. As it starts to move, say from a precisely known location, it can keep track of its motion using odometry. The robot makes an observation and updates its position and uncertainty. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 14 / 31

Probabilistic Map-based localization Probability theory → error propagation, sensor fusion. Belief representation (map/position) → discrete / continuous. Motion model → odometry model. Sensing → measurement model. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 15 / 31

Probabilistic Reasoning Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 16 / 31

Probabilistic Map Based Localization Markov Localization Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 17 / 31

Markov Localization Markov localization uses an explicit, discrete representation for the probability of all positions in the state space. Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 18 / 31

Markov Localization Markov localization uses an explicit, discrete representation for the probability of all positions in the state space. Usually represent the environment by a finite number of (states) positions: - Grid - Topological Map Janyl Jumadinova Robotic Agents (CMPSC 311) September 19, 2019 18 / 31